deliverable 5.3: economic uncertainty...

TRANSITIONS PATHWAYS AND RISK ANALYSIS FOR CLIMATE

CHANGE MITIGATION AND ADAPTATION STRATEGIES

Deliverable 5.3: Economic uncertainty appraisal

Project Coordinator: SPRU, Science Policy Research Unit, (UoS) University of Sussex

Work Package 5 Leader Organisation: ETHZ

Task 5.3 Leader: University of Graz (Karl Steininger)

Contributing organisations: BC3 Basque Centre for Climate Change, Cambridge Econometrics

(CE), Pontifical Catholic University of Chile (CLAPESUC), ETH Zurich, IBS Institute for Structural

Research, National Technical University of Athens (NTUA), Stichting Energieonderzoek Centrum

Nederland (ECN), Stichting Joint Implementation Network (JIN), Stockholm Environment Institute

(SEI), University of Graz (Uni Graz), University of Piraeus Research Center (UPRC), University of

Sussex (UoS)

Contributing authors: Rocio Alvarez Tinoco, Gordon MacKerron, Annela Anger-Kraavi, Marek

Antosiewicz, Gabriel Bachner, Paula Diaz Redondo, Luis Gonzales-Carrasco, Mikel Gonzalez-

Eguino, Francis Johnson, Jakob Mayer, Marc Neumann, Alexandros Nikas, Sotiris Papadelis, Karl

Steininger, Andreas Türk, Oscar van Vliet, Jan Witajewski, Brigitte Wolkinger, Bob van der

Zwaan

July 2017

TRANSrisk

Transitions pathways and risk analysis for climate change mitigation and adaptation strategies

GA#: 642260

Funding type: RIA

Deliverable number (relative in WP)

5.3

Deliverable name: Economic uncertainty appraisal

WP / WP number: Uncertainty and risk appraisal of policy options/WP5

Delivery due date: Month 22 originally, requested delay until month 23

Actual date of submission:

Dissemination level: Public

Lead beneficiary: ETHZ

Responsible scientist/administrator: Karl Steininger

Estimated effort (PM):

Contributor(s):

Estimated effort contributor(s) (PM):

Internal reviewer: Hector Pollitt (CE), Alev Sorman (BC3)

Table of Contents Table of Contents .................................................................................... 3 List of Acronyms ..................................................................................... 6 List of Figures ........................................................................................ 8 List of Tables ....................................................................................... 12

1 Overview ........................................................................................... 1

2 Economic Uncertainty Appraisal of Climate Change Impacts ............................. 3

2.1 Introduction .................................................................................. 3 2.2 Methods ....................................................................................... 4

2.2.1 DICE model ............................................................................... 4

2.2.2 Uncertainty- and sensitivity analysis ................................................. 5

2.2.3 Procedure of analysis ................................................................... 6

2.2.4 Computational techniques ............................................................. 7

2.3 Description of uncertain parameters .................................................... 7 2.3.1 Population ................................................................................ 7

2.3.2 GDP growth .............................................................................. 8

2.3.3 Carbon intensity ......................................................................... 8

2.3.4 Carbon cycle ............................................................................. 8

2.3.5 Non-CO2 forcing ......................................................................... 9

2.3.6 Equilibrium climate sensitivity ........................................................ 9

2.3.7 Damage function coefficient and exponent ........................................ 10

2.3.8 Pure rate of time preference ........................................................ 11

2.3.9 Time horizon ............................................................................ 11

2.4 Results ....................................................................................... 12 2.4.1 Uncertainty Analysis ................................................................... 12

2.4.2 Sensitivity Analysis ..................................................................... 14

2.4.3 Damage function as the main source of uncertainty.............................. 15

2.5 Discussion and Conclusions .............................................................. 16

3 Economic Uncertainty Appraisal of Transition Pathways ............................... 17

3.1 Overview .................................................................................... 17 3.2 Characterisation of Case Studies ....................................................... 18

3.2.1 Austria ................................................................................... 18

3.2.2 Greece ................................................................................... 18

3.2.3 Poland ................................................................................... 19

3.2.4 Netherlands ............................................................................. 19

3.2.5 United Kingdom ........................................................................ 20

3.2.6 Switzerland ............................................................................. 21

3.2.7 Chile ..................................................................................... 21

3.2.8 Kenya .................................................................................... 22

4 Economic Evaluation of Consequential Risks of Transition Pathways ................ 23

4.1 Methods ..................................................................................... 23 4.1.1 The BSAM Model ........................................................................ 23

4.1.2 The Calliope Energy Modelling Framework ......................................... 24

4.1.3 The E3ME Model ........................................................................ 25

4.1.4 The MEMO Model ....................................................................... 26

4.1.5 The TIAM-ECN Model ................................................................... 27

4.1.6 The WEGDYN Model .................................................................... 28

4.2 Austria – Switching European Iron and Steel to Carbon-Free Production ...... 29 4.2.1 Model description, data and scenario specification............................... 29

4.2.2 Techno-economic uncertainty ....................................................... 33

4.2.3 Macro-economic uncertainties ....................................................... 37

4.2.4 Social Uncertainties ................................................................... 39

4.2.5 Socio-economic uncertainties ........................................................ 42

4.2.6 Uncertainties associated with climate policy world .............................. 44

4.2.7 Model uncertainties .................................................................... 45

4.2.8 Qualitative considerations ............................................................ 47

4.3 Greece – Achieving a low-carbon power system through empowering consumers to locally produce and consume clean energy .................................................. 52

4.3.1 Applied models ......................................................................... 52

4.3.2 Economic Risks evaluated by BSAM modelling ..................................... 54

4.3.3 Economic and Social Risks evaluated by MEMO modelling ....................... 54

4.4 Poland ....................................................................................... 58 4.4.1 Applied models ......................................................................... 58

4.4.2 Economic Risks ......................................................................... 59

4.5 Netherlands ................................................................................. 65 4.5.1 Applied model .......................................................................... 65

4.5.2 Economic Risks ......................................................................... 67

4.5.3 Next steps and analysis of uncertainty ............................................. 69

4.5.4 Meeting the 2020 RES target ......................................................... 69

4.6 United Kingdom ............................................................................ 70 4.6.1 Nuclear power scenarios and models applied ...................................... 70

4.6.2 Risks ...................................................................................... 73

4.7 Switzerland ................................................................................. 80 4.7.1 Applied model .......................................................................... 80

4.7.2 Risks ...................................................................................... 80

4.7.3 Qualitative considerations ............................................................ 81

4.8 Chile ......................................................................................... 82 4.8.1 Applied model and scenarios ......................................................... 82

4.8.2 Risks ...................................................................................... 82

4.9 Kenya ........................................................................................ 84 4.9.1 Applied model and scenarios ......................................................... 84

4.9.2 Economic Consequential Risks ....................................................... 85

4.10 New insights across case studies and beyond existing literature ................ 99 4.10.1 European iron and steel transition .................................................. 99

4.10.2 The contribution of power market flexibility on the effects from increased power

self-consumption ................................................................................ 101

4.10.3 Labour market effects ............................................................... 102

4.10.4 Natural gas as a bridging fuel ...................................................... 103

4.10.5 Carbon policy implications in Chile ............................................... 104

4.10.6 Transition of the energy sector: the example of Kenya ........................ 104

5 Discussion and Conclusions for Transition Pathways .................................... 106

5.1 Uncertainty Appraisal of Climate Change Impacts ................................. 106 5.2 Uncertainty Appraisal of Mitigation Pathways ...................................... 107

References............................................................................................ 110

APPENDICES .......................................................................................... 119

List of Acronyms °C … Degree Celsius

ABM … Agent-based model

AP … Ambitious Policy

BAU … Business as usual

BF-BOF … Blast-furnace basic-oxygen furnace technology

bn … Billion

BSAM … Business Strategy Assessment Model

c.v. … Coefficient of variation

CCS … Carbon Capture Storage

CGE … Computable General Equilibrium

CH4 … Methane

CHP … Combined Heat and Power

CO2 … Carbon dioxide

COP … Conference of Parties

CRES … Center of Renewable Energy Sources

CSP … Concentrating solar power

DRI-H-EAF … Hydrogen-based direct-reduced iron electric-arc furnace technology

DSGE … Dynamic Stochastic General Equilibrium model

E3ME … Energy-Environment-Economy Macro-Econometric model

ECS … Equilibrium climate sensitivity

EI … Early Action of Industry

EPA … US Environmental Protection Agency

EPIA … European Photovoltaic Industry Association

ETSAP … Energy Technology Systems Analysis Program

GDP … Gross domestic product

GHG … Greenhouse gas

Gt … Gigaton

GW(h) … Gigawatt(hour)

HTSO … Transmission System Operator

IAM … Integrated assessment model

IEA … International Energy Agency

(I)NDC … (Intended) Nationally Determined Contribution

IPCC … Intergovernmental Panel on Climate Change

kW(h) … Kilowatt(hour)

LCPDP … Low Cost Power Development Plan

LI … Late Action of Industry

LULUCF … Land use, land use change and forestry

MC … Monte Carlo

MEMO … MacroEconomic Mitigations Options model

mio … Million

MOEM … Model for the optimal Energy Mix

MW(h) … Megawatt(hour)

N2O … Nitrous oxide

PDSP … Plasma direct steel production technology

PJ … Petajoule

ppm … Parts per million

PV … Photovoltaic

RCP … Representative concentration pathway

RES … Renewable Energy Source

RP … Reluctant policy

SCC … Social cost of carbon

sd … Standard deviation

SDE+ … Stimulering Duurzame Energieproductie/Encouraging Sustainable Energy Production

SDG … Sustainable Development Goals

SMR … Small modular nuclear reactors

SNC … Second National Communication

SRC … Standardisded regression coefficient

SSP … Shared socio-economic pathway

TFP … Total factor productivity

TW(h) … Terawatt(hour)

UNFCCC … United Nations Framework Convention on Climate Change

WEGDYN … Recursive-dynamic multi-region multi-sector computable general equilibrium model

Wm-2 … Watt per square meter

List of Figures

Figure 1: Two alternative damage functions. Source: own elaboration............................... 11

Figure 2: Global sensitivity analysis of experiment A. Source: own elaboration..................... 15

Figure 3: Global sensitivity analysis of experiment B. Source: own elaboration. .................... 15

Figure 4: The workflow for the quantitative analysis carried out by BSAM. .......................... 24

Figure 5: Different CO2 price specifications [€2011/tCO2] implemented in the reference and counterfactual model runs. .................................................................................. 29

Figure 6: Change in regional market price of Iron and Steel relative to reference WEGDYN model run ............................................................................................................... 35

Figure 7: Change in regional Iron and Steel sector output relative to reference WEGDYN model run ................................................................................................................... 36

Figure 8: Change in regional gross domestic product relative to reference WEGDYN model run. 38

Figure 9: Change in regional welfare (Hicks’ian equivalent variation) relative to reference WEGDYN model run. ..................................................................................................... 39

Figure 10: Change in regional unemployment rate (skilled labour market) relative to reference WEGDYN model run. .......................................................................................... 41

Figure 11: Change in regional unemployment rate (unskilled labour market) relative to reference WEGDYN model run. .......................................................................................... 42

Figure 12: Change in regional gross domestic product (top row) and regional welfare (bottom row) as percentage difference to reference WEGDYN model run for "high cost" (left panels) and “low cost” (right panels) shown for SSP2 (solid lines), SSP3 and SSP5 (ranges), given a “reluctant policy world”. ......................................................................................................... 44

Figure 13: Welfare implications in AUT for high cost (left) and low cost specification (right) varying the underlying climate policy world. Reluctant policy (RP - 46€/tCO2 globally); Ambitious policy (AP - 138€/tCO2 globally); Ambitious EU policy (APEU - 138€/tCO2 in EU and 46€/tCO2 in the rest of the world). ..................................................................................................... 45

Figure 14: Areas of risks for the energy pathway......................................................... 48

Figure 15: Areas of risks for the iron and steel pathway. ............................................... 48

Figure 16: The estimated effect of electricity storage on wholesale electricity prices. ........... 54

Figure 17: Effect of RES Deployment on GDP. ............................................................ 56

file://fileserver.epu.ntua.gr/Projects/TRANSRISK/09%20Technical/WP5%20Uncertainty%20&%20Risk/D%205.3/D5.3_Economic_Uncertainty_Appraisal(FINAL_v1.0)140817%20-%20no%20version%20control.docx#_Toc23242576file://fileserver.epu.ntua.gr/Projects/TRANSRISK/09%20Technical/WP5%20Uncertainty%20&%20Risk/D%205.3/D5.3_Economic_Uncertainty_Appraisal(FINAL_v1.0)140817%20-%20no%20version%20control.docx#_Toc23242576

Figure 18: Effect of RES Deployment on Investment. .................................................... 56

Figure 19: Effect of RES Deployment on the Unemployment Rate. .................................... 57

Figure 20: Effect of RES Deployment on the Activity Rate. ............................................. 57

Figure 21: Distribution of workers’ productivity in four economic sectors. .......................... 59

Figure 22: Production of electricity by source [TWh]. Source: Output of the MOEM model for a stringent policy (as described in the text). ............................................................... 60

Figure 23 Production of electricity by source [TWh]. Source: Output of the MOEM model for the reference case (as described in the text) ................................................................. 61

Figure 24: Total costs of energy generation (including fuel, investment and operation and maintenance) and Required Investment. Source: output of the MOEM model. ...................... 61

Figure 25: Two types of distribution of workers’ productivity in the sector of services. In the case of distribution with homogenous workers (blue line) almost all workers in the population have the same (medium) productivity in the service sector. In the case of distribution with heterogeneous workers, some workers have extremely high productivity and some workers have extremely low productivity in this sector. .................................................................................. 62

Figure 26: Wages in mining and energy. ................................................................... 63

Figure 27: Average wage in remaining sectors. ........................................................... 63

Figure 28: Gross domestic product development. ........................................................ 63

Figure 29: Investment development. ....................................................................... 63

Figure 30: Employment development. ..................................................................... 64

Figure 31: Unemployment rate. ............................................................................. 64

Figure 32: The actual system price under the three scenarios ......................................... 67

Figure 33: The system marginal price under the three scenarios. ..................................... 68

Figure 34: The subsidy costs under the three scenarios. ................................................ 68

Figure 35: The taxation losses due to self-consumption of electricity under the three scenarios. ................................................................................................................... 69

Figure 36 UK electricity generation with nuclear capacity expanding to 40 GW in 2050 (Scenario B Nuclear expansion, measured in thousand Gigawatt hours per year) ................................. 75

Figure 37 UK employment for nuclear expansion (SB) variant scenarios, compared to the baseline scenario ......................................................................................................... 76

Figure 38 UK CO2 emissions for nuclear expansion (B) variant scenarios, compared to the baseline scenario. ........................................................................................................ 76

Figure 39 UK electricity generation with nuclear phased out by 2035 (Scenario A) measured in thousand Gigawatt hours per year. ......................................................................... 77

Figure 40 Change in UK electricity prices for no new nuclear (A) and variant scenarios compared to baseline. .................................................................................................... 78

Figure 41 UK employment for the no nuclear scenario variations (SA) and for the variations of this scenario compared to baseline. ............................................................................. 78

Figure 42 UK CO2 emissions for no new nuclear (A) variant scenarios, compared to the baseline scenario. ........................................................................................................ 79

Figure 43: Ranges of weighted LCOE for the scenarios in the short term (2035). The costs have been calculated using technology cost projections of various authors for renewable technologies (see source publication). The costs in importing scenarios (North Sea and Morocco) are calculated for two levels of carbon consumption: (B) gas as a bridging fuel, as per the Swiss Energy Strategy 2050, and (C) carbon free. The Domestic scenario is gas intensive and does not include imports. LCOE do not include externalities. Source: Díaz et al, 2017............................................ 81

Figure 44: Share of households in energy poverty assuming no growth in disposable income. .... 84

Figure 45: GHG emissions in Kenya, according to its SNC projections, including (left panel) and excluding (right panel) contributions from land use, land use change and forestry. ............... 87

Figure 46: Final energy consumption projections per carrier in Kenya until 2050, for each TIAM-ECN scenario run. ............................................................................................. 89

Figure 47: Final energy consumption projections per sector in Kenya until 2050, for each TIAM-ECN scenario run. ................................................................................................... 90

Figure 48: Electricity production capacity projections per technology until 2050, for each TIAM-ECN scenario run. Inset: zoom-in on the capacity mix until 2030...................................... 91

Figure 49: Emission projections per GHG species, for each TIAM-ECN scenario run until 2050. ... 92

Figure 50: CO2 emission projections per sector until 2050 for each TIAM-ECN scenario run. ...... 93

Figure 51: Comparison of GHG emission projections in the GoK: BAU and TIAM-ECN: REF baselines. ................................................................................................................... 94

Figure 52: GHG emission projections for all TIAM-ECN scenarios until 2050, as well as those for the BAU and NDC scenarios developed by the GoK until 2030. .............................................. 96

Figure 53: Relative change in total energy system costs in Kenya with respect to REF until 2050. ................................................................................................................... 97

Figure 54: Cumulative distribution function of popadjust. ........................................... 121

Figure 55: Cumulative distribution function of ga0. ................................................... 121

Figure 56: Cumulative distribution function of gsima. ................................................ 122

Figure 57: Cumulative distribution function of mueq. ................................................. 122

Figure 58: Cumulative distribution function of fx1. .................................................... 123

Figure 59: Cumulative distribution function of t2xCO2. ............................................... 123

Figure 60: Cumulative distribution function of a2. .................................................... 124

Figure 61: Cumulative distribution function of prtpt. ................................................. 124

Figure 62: Cumulative distribution function of timeh. ................................................ 125

Figure 63: Gap between regionally standardised price levels of refinery products (P_C) and electricity (ELY) in the reference model run of the iron and steel transition assessed by the WEGDYN model. ............................................................................................. 126

Figure 64: Gross unit cost gap as %-point difference to net unit cost gap (as presented in Table 7). ................................................................................................................. 126

Figure 65: Regional economic growth rate per anno between 2010-2050 distinguished by shared socio-economic pathways according to the IIASA SSP Database (2017). ............................ 127

Figure 66: Regional effective labour force growth rate per anno between 2010-2050 distinguished by shared socio-economic pathways according to the IIASA SSP Database (2017). Note: We assume labour efficiency gains of 1% per anno for all regions. ................................................ 127

Figure 67: Regional multi factor productivity growth rate per anno (2010-2050). Note: We applied an iterative approach in order to meet SSP specific economic growth rates. ..................... 128

List of Tables

Table 1: Procedure of uncertainty- and sensitivity analysis. ............................................ 6

Table 2: Key inputs and target outputs. ................................................................... 12

Table 3: Summary statistics of experiment A. ............................................................ 13

Table 4: Summary statistics of experiment B. ............................................................ 13

Table 5: Comparison of experiments. ...................................................................... 16

Table 6: Scenarios emerging from different combinations of policy ambitiousness and timing of industry. ........................................................................................................ 30

Table 7: Unit cost structures of different steel production technologies (net of taxes). In order to account for a techno-economic range of alternative technologies we assume an electricity price of 5€cents/kWh for the otherwise more expensive DRI-H-EAF route and 3€cents/kWh for the PDSP route. ........................................................................................................... 31

Table 8: Scenarios for PV installed capacities and market share of small-scale electricity storage. ................................................................................................................... 52

Table 9 Modelled pathways and variants ................................................................. 73

Table 10: The effect of carbon prices on energy price and energy poverty in 2020. ............... 83

Table 11: Regional longer term capital depreciation rates deltaL; calculated from Penn World Table data (Feenstra et al., 2015) using annual depreciation rates delta, capital stocks KS in the period of 1990-2011 and the equation shown below the table. ...................................... 128

Table 12: Regional unemployment rates benchmark year 2011 for unskilled (ur_unl) and skilled labour (ur_skl) (WB, 2015); own calculations. .......................................................... 129

Table 13: Regional aggregates of the WEGDYN model. ................................................ 130

Table 14 UK Nuclear power expansion as one of the options to low carbon pathways for climate change mitigation ........................................................................................... 138

Table 15 The no new UK Nuclear power scenario ...................................................... 139

D5.3: Economic Uncertainty Appraisal Page 1

1 OVERVIEW The aim of this report is to evaluate first, the potential range of climate impacts and second, key uncertainties in the socioeconomic energy and climate system that determine the impact of low-carbon mitigation pathways. In particular, we explore uncertainty and respective (ranges of) energy and climate system input parameters used in quantitative model evaluations. This report aims to shed light on the drivers for uncertainties, including assumptions made, and to understand diverging results across different model approaches.

Types of uncertainties may include future, parameter (choice of values), structural/model uncertainty, measurement or policy uncertainties. Some of the uncertainties can be incorporated as alternative scenario runs with no predetermined end-result. Some of the models are also able to quantify risks, including the simulation of alternative scenarios with probability distribution.

The report gives important insights into the strengths and limitations of different models based on their application in different TRANSrisk case studies. It examines to what extent qualitative methods are useful and needed to complement the assessment of uncertainties and risks. It considers the economic evaluation of economic, social, technological, environmental, but also institutional and political uncertainties and, as far as possible, risks.

The report intends to give policymakers a better understanding on the range of impacts that can be triggered by both climate change and climate mitigation policies, and builds upon the application of different models. It thus also identifies the need for complementary policies to deal with possible impacts and risks.

With respect to terminology we apply the core terms in their meaning as defined in TRANSrisk Deliverable D5.1. Impacts can be known with certainty or subject to uncertainty (at various degrees thereof). In the latter case, beneficial impacts are labelled opportunities, while the space of detrimental ones defines risks. Such risks can be of different types (economic, social, political, technological, environmental), and can be either connected to the very implementation (risks that constitute barriers to implementation) or – once a policy or strategy has been implemented – to consequences of such policies (consequential risks).

Qualitative analysis can succeed in identifying the broad range of different uncertainties, however, the evaluation of their magnitude by qualitative approaches has considerable limitations. Quantitative analysis seeks to supply ranges of uncertainties along different dimensions considered especially relevant. Modelling and quantification requirements imply also limits on uncertainty analysis, however. Quantitative approaches first lend themselves to quantify parametric uncertainty (i.e. uncertainty on actual realisation of core parameters that models draw upon). While the choice of parameters analysed was inspired by stakeholder interaction, not all models yet incorporate all parameters of interest. Beyond parameter uncertainty, by applying different model types, we can also identify structural/model uncertainty, e.g. for the Austrian case study when we compare an econometric and a computable general equilibrium approach.


Model-inherent uncertainty is governed by (i) the degree of temporal and spatial resolution, (ii) the perspective of the approach (top-down or bottom-up), (iii) the selection of macroeconomic closure (where relevant, in our analysis for the econometric E3ME, the dynamic stochastic general equilibrium MEMO and the computable general equilibirum WEGDYN models)1, and (iv) the solution typology (optimisation / statistical / simulation and deterministic / stochastic, respectively). On the last issue: there are different types of uncertainties that can be covered with optimization models (such as the DSGE approach of the MEMO model below, or the technical optimization of the TIAM-ECN model, or the recursive-dynamic CGE approach of the WEGDYN model), with statistical models (such as the econometric E3ME model), or with simulation models (such as the agent based model BSAM). Newly available information, as one source of uncertainty, for example, can be covered in all optimisation models applied in this deliverable, as none of them assumes perfect foresight. The dimension of immediate impact, however, differs between a recursive dynamic approach where optimization is only within period (such as the WEGDYN model), and new information impacts are stepwise, and an intertemporally optimizing approach (as the MEMO model) with potential additional repercussions on already the respective period where new information becomes available. Within the TRANSrisk project the current deliverable represents a first presentation of uncertainties analysed across case studies and quantitative models, with further uncertainty appraisal to be reported in related academic publications and to be gained in Work Package 6 (and reported in respective Deliverables).

This report is structured as follows. Chapter 2 focuses on climate change impacts, their uncertainty and economic evaluation of the latter in terms of social costs of carbon. Chapters 3 and 4 focus on the economic uncertainty appraisal of low-carbon transitions. Chapter 3 reports the framework of analysis and case study details to give the context for which the analysis is carried out, chapter 4 reports the detailed evaluations, structured by case studies. Finally, chapter 5 discusses the overall conclusions for transition pathways, in Europe and globally.

1 Each of the models is explained in detail in section 4.1, the energy-environment-economy macro-econometric model (E3ME), the MacroEconomic Mitigations Options (MEMO) model, the TIMES Integrated Assessment Model, operated at ECN (TIAM-ECN), the dynamic computable general equilibirum model operated at Wegener Center (WEGDYN), the Business Strategy Assessment Model (BSAM), and the Calliope energy system models framework


2 ECONOMIC UNCERTAINTY APPRAISAL OF CLIMATE CHANGE IMPACTS

2.1 Introduction The social cost of carbon (SSC) is an economic concept that has become very influential in the current climate policy debate. The SCC tries to capture the future impacts of climate change, i.e. the external cost of carbon. More precisely, the SCC measures the discounted value of the change in economic welfare that results from an additional unit of carbon emission. The SCC is conditional on a specific trajectory of future global emissions and other economic, biophysical and climatic processes. SCC values have been traditionally calculated using Integrated Assessment Models (IAMs) of the climate and the economy, although other methods such as expert elicitation are also being explored due to the difficulties to estimate future damages of climate change (Pindyck 2013, 2016). SCC has become increasingly important in regulation, and in some cases is used to determine (in benefit-cost analysis) whether domestic policies pass (Pizer et al. 2014). Therefore, better understanding the uncertainties associated with SSC has been proposed as key area for research and as an opportunity to advance in climate change economics (Burke et al. 2016).

There is a lot of literature that provides estimates for SCC for a variety of different assumptions (see van den Bergh and Botzen 2014). The range of estimations of SCC values is very large due to the uncertainty inherent in many of the processes involved, and also due to different assumptions used by different models. Recently, for example, some authors such as Nordhaus have increased their central estimation; in the case of Nordhaus, using the DICE model for determining the SCC from $17 to $31 per ton of CO2 (see Nordhaus 2014 and Nordhaus 2017). In 2010 the US Environmental Protection Agency (EPA) created an expert group that - based on the results of three IAM models (FUND, PAGE and DICE) - provides different SCC estimates which were revised and updated in 2013 (IAWG, 2013). The value estimated for SCC by the year 2015 were US$11, US$36 and US$52, associated with the 5%, 3% and 2.5% discount rates. These estimates, especially the central value (US$36) have played a crucial role in US climate policy, as more than $1trillion of benefits have been written into policies that use SCC in their economic analysis.

In contrast, some countries such as the UK are proposing to move away from the SCC concept. In 2002 a report by the UK Government Economic Services initially recommended a SCC of 19 £/tCO2 (with a range of 10-38 £/tCO2). However, the report suggested finding a more pragmatic approach, arguing that the consideration of a low probability catastrophic climate change event would substantially increase the value of SCC. Finally, the UK government (2009), proposed another approach based on the estimated cost of achieving a specific mitigation target. Based on marginal abatement cost curves a short-term price of carbon of £25 in 2020 (with a range of £14-£31) and long-term prices of £70 in 2030 and £200 in 2050 were suggested. These values are currently being reviewed in the process of the implementation of the UK Low Carbon Transition Plan that plans to reduce greenhouse gas emissions by at least 80% in 2050 from 1990 levels.


Here we study how uncertainty of the key parameters of Integrated Assessment models affect the outcomes of the latter and how the cascade effects of uncertainties are transferred to climate damages and SCC values. Recently, a multi-model comparison study (Gillingham et al. 2015), assessed three main uncertain parameters (population growth, GDP growth and equilibrium climate sensitivity) with six different IAM models (DICE, GCAM, WITCH, MERGE, FUND, IGSM). They found that the influence of the uncertainty of these parameters was “much greater” than the uncertainty2 across models (model structure).

We use the DICE model (one of the models from the multi-model comparison study cited above), and perform an uncertainty and a global sensitivity analysis. The parameters selected are informed by previous work done by Butler et al. (2014) and other authors that already showed which are the more influential parameters in DICE (using samples of quasi-randomly uniform distributions with lower and upper bounds). In this work, we review a range of potential probability distributions for the uncertainty of key input parameters in order to obtain probability distributions for key output variables, including SCC.

This chapter 2 is structured as follows. Section 2.2 provides an overview of the model and the methods used for uncertainty and sensitivity analysis. Section 2.3 presents the choice and distributions selected for the different parameters. Section 2.4 discusses the key results and findings of the study in terms of implications for SCC. Finally, section 2.5 draws conclusions.

2.2 Methods The idea of conducting an uncertainty and sensitivity analysis is twofold. First, we want to understand how uncertainty in key model parameters (for inputs, see Butler et al. 2014) are propagated and to analyse how large or small the uncertainty of the target variables is (uncertainty analysis). Second, we want to know which of the uncertain parameters are responsible for causing uncertainty in each of the target variables (sensitivity analysis). We capture a set of key target (or output) variables, but focus our attention on the social cost of carbon. In this section, we present an overview of the model, the methods used for uncertainty and sensitivity analysis, the procedure that we are following and the computational techniques that we are using.

2.2.1 DICE model

The DICE model is a well-known IAM model that is being reported in the IPCC Assessment Reports (IPCC 2014) and in the U.S. government Interagency Working Group Report on the Social Cost of Carbon (IAWG 2013). DICE represents the economic, policy and scientific aspects of climate

2 Although they mention explicitly that that is not the case for SSC, the choice of the damage function of each model is clearly the key difference for the results, which at the end is also a choice of the function and parameters selected for the damage function, not a characteristic of the model itself.


change. The model captures the main socio-economic and technical processes (population, economic growth, decarbonisation) that generate CO2 emissions and other non-CO2 forcings and also the main natural process, such as the carbon cycle and how the accumulation of carbon in the atmosphere increases radiative forcing and leads to warming of the earth surface. The increase in temperature change is associated with economic damages (in terms of loss of GDP) through a damage function, one of the most sensitive parameters/assumptions of IAM model as it captures the monetary value of losses with increasing temperature. Finally, the social cost of carbon can be calculated.

2.2.2 Uncertainty- and sensitivity analysis

2.2.2.1 Uncertainty analysis

For the propagation of uncertainty, we apply standard Monte Carlo simulation. In the first step, parameters are characterised through probability distributions. Then random samples are drawn from these distributions and the model is run multiple times (e.g. n = 1000). In this way, we obtain n realisations for each target variable. Then we can analyse these distributions visually through histograms or cumulative distributions or numerically through summary statistics. For the summary statistics, we include mean, standard deviation (sd), coefficient of variation (c.v.) and 25%, 50% and 70% percentiles.

2.2.2.2 Sensitivity analysis

The goal of sensitivity analysis is to examine how variation in a model output (variables) can be apportioned to variation of model inputs (parameters). In many cases, it is unclear which sensitivity method to select à priori. The appropriate selection depends on the goal of the sensitivity analysis, on the model structure, the uncertainty ranges of the parameters and the computational cost. Most sensitivity analysis applications within the scientific literature are based on changing one parameter at a time (local sensitivity analysis) and often assessing the impact of a small perturbation/deviation to the parameter. Global sensitivity analysis relaxes these assumptions by i) looking at the effect of changing a parameter while all others are also changing and ii) looking at the effect of varying a parameter across its entire distribution. This is especially relevant for modelling integrated systems where non-linearity and parameter interactions may be abundant. An example testing five different sensitivity analysis methods can be found in Neumann (2012).

Using the terminology proposed by Saltelli et al. (2004), we are interested in “factors prioritisation” to identify the most important parameters, i.e. the parameters, which if known, would be expected to lead to the largest reduction of variance in the model output. We use a regression-based method (Standardised Regression Coefficients, SRC). The rationale of regression-based methods is to perform a Monte Carlo (MC) simulation of the model by random sampling of the parameters and then fitting a multivariate linear model to the output of the MC simulation


(eq. 1). The standard deviations σ of both the model output Y as well as the parameters xi are

used to obtain the standardised regression slopes iβ

(eq. 2).

i iY b x a= ⋅ +∑ (1)

ixi i

y

σβ b

σ= ⋅

(2)

According to (Saltelli, 2004) iβ

is a valid measure of sensitivity if the coefficient of determination

(R2) is higher than 0.7. When the model is linear, the Standardised Regression Coefficients iβ

are

equal to results obtained by local sensitivity analysis. The 2

iβ approximates the first-order variance contribution; therefore, regression based methods pertain to the objective factors prioritisation. Methods are available to calculate the required number of simulations, e.g. Hsieh et al. (1998). Typical numbers found in literature are N = 500 to 1000. Here we repeated the analysis several times using n = 1000 and obtained stable results.

2.2.3 Procedure of analysis

The procedure followed in this analysis can be found in Table 1.

Table 1: Procedure of uncertainty- and sensitivity analysis.

Step Description Tasks

1 Identify target variables

Choose a model and select the target variables for which the analysis should be conducted.

2 Identify and quantify uncertain parameters

Pre-select parameters to be included in uncertainty and sensitivity analysis based on expert judgment.

Quantify uncertainty through literature review and expert elicitation. Specify probability distributions used.

Characterise cause of uncertainty, e.g. epistemic uncertainty, natural variability, a potential choice range

3 Uncertainty analysis

Uncertainty propagation: random sampling of parameter values and propagation to target variables through Monte Carlo simulation.

Visual analysis of distributions of target variables: histograms and empirical cumulative distribution functions


Descriptive statistics: mean, standard deviation, minimum, maximum, quantiles: q = 0.25, 0.5, 0.75, coefficient of variation.

Analysis and interpretation.

4 Sensitivity analysis

Compute Standardised Regression Coefficients (SRC). Interpretation of SRC2: which parameters are determining how much of the variance of the variables? Interpretation of R2: validity of assumption of linearity.

2.2.4 Computational techniques

We use the DICE 2016R model version in GAMS code (available here

http://www.econ.yale.edu/~nordhaus/homepage/) and for the uncertainty and sensitivity

analysis we developed our own code in R (R Core Team 2017).

2.3 Description of uncertain parameters We identify ten key parameters (based on Butler et al. 2014, see Table 1and Table 2). For each

parameter, we explain a) the main process that is captured, b) the sources of information we have

used to provide an uncertainty range and c) the specific parameter values that are changed in the

original DICE model. In some cases, the uncertainty range is provided by information on models or

model-databases and in some cases through expert elicitation, depending on the nature of the

process in consideration. Some of these parameters are purely scientific or descriptive (such as

the equilibrium climate sensitivity) and others normative (or prescriptive, such as discount rates).

In some cases, uncertainty is likely to decrease in the future (population estimates), and in others

it is not (equilibrium climate sensitivity).

2.3.1 Population

This process captures the future growth of global population. We use the probabilistic global

population estimations provided by United Nations Population Division (UN 2015) that build upon

models of fertility change, mortality and migration for each country. Quantile data are taken from

the UN database. For 2100 these values (in billions) are the following: 9.49 (q=0.025), 10.0

(q=0.10), 11.2 (q=0.50), 12.5 (q=0.90), 13.2 (q=0.975). We use the R package risk distributions to

find an appropriate theoretical distribution to fit to these quantiles. From 17 distributions tested

we obtain the best correspondence with a lognormal distribution (Meanlog = 16.23, sdlog = 0.0855)

characterised with mean = 11.2 and standard deviation = 0.96. The main parameter of the DICE

http://www.econ.yale.edu/%7Enordhaus/homepage/


model that captures this process is popadjust that provides the stabilization value for global

population in 2100.

2.3.2 GDP growth

This process captures the future growth of global GDP. The main parameter that governs this

process in the DICE model is ga0, which is an exogenous measure for the future growth of total

factor productivity (TFP). We use the survey from Drupp et al. (2015), who obtained expert

opinions about uncertainty in global annual growth rates for the period 2010–2100. The resulting

combined normal distribution has a mean annual growth rate of 1.7 % with a standard deviation

of 0.9 %. Although these numbers are higher than in similar studies such as in Gillingham et al.

(2015) (2.29+-1.15), the paper from Drupp et al. (2015) has the largest dataset available with a

specific question to this issue. We also truncated the distribution (with a lower = 0.1 and upper =

3.5 value) to avoid explosive growth in some of the scenarios with very long-time horizon, which

allow us also to keep the basic structure of growth dynamic of DICE model unaltered.

2.3.3 Carbon intensity

This process captures the future decarbonisation of the economy due to changes in energy and

carbon intensity. Carbon intensity is a measure of the global CO2 emissions per unit of global GDP.

This value has decreased from 0.73 kgCO2/US$ in 1960 to 0.36 kgCO2/US$ in 2015, mainly due to

energy intensity improvements. In DICE this process is captured by sigma, the average annual

percent decrease in carbon intensity. We use the IPPC AR5 database, where 180 scenarios for

carbon intensity under a reference (no climate policy) pathway are available until 2100 and obtain

a mean of -1.33% and a standard deviation of 0.41%. We truncate the corresponding normal

distribution at the lowest (-2.55%) and highest (-0.45%) values of the database.

2.3.4 Carbon cycle

This parameter regulates the carbon fluxes between carbon reservoirs. This uncertainty is relevant

because radiative forcing will depend on the accumulation of carbon in the atmosphere, which is

also related to the land and the ocean sinks. The uncertainty range of the carbon cycle is based

on the uncertainty shown in the last IPCC report (IPCC 2013, WG1, Chapter 12, p. 1096). For a

given exogenous emission pathway (RCP8.5) with a concentration of 985 ppm by 2100, the range

of uncertainty for that year using the MAGICC6 model in the atmosphere is +/- 97ppm (68%) and -


191 and +164 (794-1149, 90% range). These results are very close to what was obtained in the

CMIP5 by 11 Earth System models (IPCC 2013). Following the approach of Nordhaus (2016) this

uncertainty range is introduced at the intermediate reservoir which is governed by mueq which

captures the land-atmosphere carbon cycle. We characterise mueq with a truncated normal

distribution characterised by the original normal distribution with mean = 0 and sd = 97 and

truncation at -200 and 200.

2.3.5 Non-CO2 forcing

This parameter captures future emissions of non-CO2 greenhouse gases. We obtained the

uncertainty range of this exogenous parameter from IPPC AR5 database where 180 scenarios for a

reference pathway (no climate policy) are available. The non-CO2 radiative forcing for 2015 in

DICE is 0.5 Wm-2 and is projected to increase to 1 Wm-2 (consistent with RCP6, see also Rogelj et

al. 2016). According to AR5 database the mean value for 2100 is 1.46 Wm-2 with a standard

deviation of 0.24 Wm-2. We change the exogenous parameter fex1 in 2100 in the DICE model

following the AR5 database range. The distribution of fex1 is based on resampling with

replacement from the 180 original data points with mean of 1.46 and sd of 0.24 (min = 0.64 and

max = 2.04).

2.3.6 Equilibrium climate sensitivity

The Equilibrium Climate Sensitivity (ECS) parameter is one of the key parameters in climate

science (see Deliverable 4.1 of this project). ECS is defined as the equilibrium change in global

temperature due to a doubling of atmospheric CO2 over its preindustrial value. This measure is

typically characterised as a distribution due to underlying uncertainty on feedbacks of the climate

system. According to the IPCC’s Fifth Assessment Report (IPCC 2013, WG1, Technical summary, p.

81), estimates of the ECS indicate the probability density distribution of ECS peaks at around 3°C,

with a long tail of small but finite probabilities of much larger temperature increases. According

to the IPCC, estimates of the ECS indicate that it is likely to be in the range of 1.5°C to 4.5°C

(with high confidence), extremely unlikely to be less than 1°C (high confidence) and very unlikely

to be greater than 6°C (medium confidence). Following Gillingham et al. (2015), we use a log-

normal distribution fitted to the distribution proposed by Olsen et al. (2012). The lognormal

distribution (MeanLog = 1.11, sdLog = 0.26) has a mean = 3.13 and a standard deviation = 0.843.

It is important to mention that the uncertainty range of ECS has not been reduced substantially in


the past three decades and it is not expected to be reduced in the near future (Roe and Baker

2007). The parameter that captures this process in DICE model is t2xCO2.

2.3.7 Damage function coefficient and exponent

Different studies have shown that the results of IAM models are very sensitive to the choice of the

damage function (Ackerman, Stanton, and Bueno 2010). Damage functions are recognised to be

the “weakest link” in the economics of climate change (Pindyck, 2013).

The damage function is typically expressed in the form of D(t)=a*deltaT(t)^b. Where D(t) is the

damage at time t, as a function of global average temperature change T(t) at time t and “a” and

“b” are the coefficient and exponent of the damage function.

One of the most well known damage functions is the one used by Nordhaus (Nordhaus and Sztorc

2013), which has been recently used by the US Environmental Protection Agency (IAWG 2010,

2013). In this version of the model the exponent “b” is set to b = 2. In an uncertainty assessment

of his own model, Nordhaus (2016) assumes an uncertainty range for the coefficient a. For this

coefficient, we apply the normal distribution of Nordhaus (2016) with mean = 0.236 and sd = 0.118

with truncation (lower = 0.01, upper = 0.708). In the DICE model the abbreviation a2 is used for

the damage coefficient.

Many authors have stressed that the damage function should also capture the possibility of “tipping

points”. Weitzman et al. (2012) proposed a damage function based on a panel of 52 experts. These

authors mention different tipping points such as irreversible meltdown of the Greenland ice sheet,

disintegration of the West Antarctic ice sheet, reorganization of the Atlantic thermohaline

circulation, among others. Many modellers caution that at higher temperatures the damage

functions might go beyond their useful limits. To account for these points we propose to run a

second experiment with an alternative damage function (coefficient a2 and exponent a3) that

does include tipping points (see below), starting at around 2 ºC (a2=0.001 and a3=5


Figure 1: Two alternative damage functions. Source: own elaboration

2.3.8 Pure rate of time preference

Another major source of uncertainty is the rate at which the future damages and cost of climate

change is discounted to the present. Drupp et al. (2015) record the values of key parameters

associated with intergenerational preferences based on a survey of 197 experts. For the pure rate

of time preference, they obtain a mean of 1.1% and sd of 1.4% with values ranging from 0% to 8%.

For our analysis, we assume a homogenous range of 0.05% to 5%. Therefore, this reflects the space

in which the choice would be made, without a weighting of any particular values. The parameter

that capture the pure rate of time preference in DICE model is prtp.

2.3.9 Time horizon

Finally, another important aspect of the analysis is the time horizon of the analysis. The integrated

assessment models estimate damages occurring after the emission release and into the future,

often as far out as 2200 or even more. The present value calculation, for social cost of carbon, for

example, will be higher for higher time horizons. The range for the last year considered in this

analysis is from 2100 to 2200, using a uniform discrete function. The parameter of the DICE model

is timeh.

0%

10%

20%

30%

40%

50%

0 1 2 3 4 5 6

GD

P lo

ss (%

)

Global Temperature Change (°C)

NordhausWeitzman


2.4 Results This section shows results for two experiments:

• Experiment A: the first experiment includes uncertainty in all the parameters of Table 2,

including the damage coefficient a2. However, the value of the exponent of the damage

function is fixed to a3 = 2, as in Nordhaus 2016.

• Experiment B: the second experiment includes uncertainty in all the parameters of Table

2, with the exception of the coefficient and exponent which are replaced in order to

reproduce the Weitzman damage function (a2=0.001 and a3=5)

We list the main inputs (10 parameters) and the key outputs (7 variables) in Table 2.

Table 2: Key inputs and target outputs.

Key inputs (parameters) Target outputs (variables)

1. Population (popadjust) 2. GDP growth (ga0) 3. Carbon intensity (gsigma) 4. Non-CO2 forcings (fex1) 5. Carbon Cycle (mueq) 6. ECS (t2xCO2) 7. Damage coefficient (a2) (only Exp. 1) 8. Damage exponent (a3) 9. Discount rate (prtp) 10. Time horizon (timeh)

V1: CO2 emissions (GtCO2) in 2100

V2: CO2 concentrations (ppm) in 2100

V3: Radiative Forcing (Wm-2) in 2100

V4: Temperature change (ºC) in 2100

V5: GDP per capita in 2100 (US$)

V6: Climate damages in 2100 (% of GDP)

V7: Social cost of carbon in 2020 (US$)

2.4.1 Uncertainty Analysis

We present the summary statistics of the target outputs for the two experiments (

Table 3 and Table 4). The experiment A is characterised by a temperature change of 4.6 +/- 1.5

ºC. This increase of temperature generates climate damages of 5% +/- 2.8% of GDP. The 2020 SCC

is 25.6 +/- 30 US$/tCO2. The 25-75% interquartile range for SCC is between 8.6 and 31.7. These

results are similar to those obtained by Nordhaus (2016).


Table 3: Summary statistics of experiment A.

Min q=0.25 q=0.50 mean q=0.75 Max Stdv c.v

CO2 emissions (GtCO2) 11.9 51.9 92.7 112.4 163.9 419.7 74.9 0.67

CO2 concentrations (ppm) 520.5 751.6 903.4 966.3 1141.0 1932.0 272.7 0.28

Radiative forcing (Wm-2) 4.8 6.8 7.8 7.9 9.0 12.1 1.5 0.19

Temperature change (ºC) 1.9 3.9 4.5 4.6 5.2 7.9 1.0 0.21

GDP per capita (US$) 15920 46020 76670 94090 128000 375100 65400 0.70

Damages (% of GDP) 0.13 2.76 4.47 5.00 6.72 19.74 2.98 0.60

SCC in 2020 (US$) 0.5 8.6 14.9 25.6 31.7 333.3 30.3 1.18

Table 4: Summary statistics of experiment B.

min q=0.25 q=0.50 mean q=0.75 Max Stdv c.v

CO2 emissions (GtCO2) 6.8 30.7 53.5 70.0 94.9 341.0 53.5 0.76

CO2 concentrations (ppm) 463.9 647.7 749.1 792.6 892.0 1718.0 195.7 0.25

Radiative forcing (Wm-2) 4.3 6.0 6.8 6.9 7.7 11.5 1.3 0.18

Temperature change (ºC) 1.7 3.5 4.0 4.1 4.6 7.5 0.8 0.20

GDP per capita (US$) 693 5661 10050 14200 18150 127000 13197 0.93

Damages (% of GDP) 11.4 70.5 79.1 75.8 84.7 94.4 12.7 0.17

SCC in 2020 (US$) 49.7 445.6 732.1 1053.0 1379.0 3936.0 846.9 0.80

The experiment B leads to a similar temperature change of 4.1 +/- 0.8 ºC. However, the damages

by 2100 are vast: 75% +/- 12% of GDP. The 2020 SCC is 1053 +/- 846 US$/tCO2. The 25-75%

interquartile range for SCC is between 445 and 1379 US$/tCO2.

These results show that propagation of uncertainty leads to a high dispersion for many of the

target outputs. In terms of coefficient of variation, the dispersion observed in the variables is


similar across the two experiments. Although the relative dispersion does not change much

between the experiments, the values for the social cost of carbon are multiplied by a factor of 40

or even more.

2.4.2 Sensitivity Analysis

After analysing the main statistics of the uncertainty propagation, we now study the contribution

of different inputs towards the variation of the main target outputs. To this end we assess the 2iβ (the squared standardised regression coefficients (SRC)) which approximate the first order effects

of the variance decomposition (see section 2.3).

The results reported in Figure 2 show that CO2 emissions, CO2 concentration, and radiative forcing

in 2100 are explained by more than 75% by the uncertainty in GDP growth (ga0). Although

uncertainties related to population, carbon intensity, non-CO2 forcing and carbon cycle also play

a role, their impact is quite low. In the case of temperature change the role of GDP growth is still

quite high (34%), but lower than the effect of the ECS parameter (t2xCO2), which explains 52% of

the variance. The damages are explained by the GDP growth (15%), the ECS parameter (22%) and

mainly due to damage coefficients parameter (a2), that explains 49%. The SCC is mainly explained

due to the discount rate (prtp) (33%), the damage coefficients parameter (15%) and by the ECS

parameter (5%), being the rest explained by other inputs uncertainty and higher-order

interactions.

The results for experiment B are similar to those of experiment A in view of CO2 emissions, CO2

concentration radiative forcing and temperature change in 2100. In experiment B, the assumption

of linearity becomes critical for GDP per capita and damages. Uncertainty in SCC is now being

mainly explained by the discount rate (prtp).


Figure 2: Global sensitivity analysis of experiment A. Source: own elaboration.

Figure 3: Global sensitivity analysis of experiment B. Source: own elaboration.

2.4.3 Damage function as the main source of uncertainty

Finally, the influence in the choice of damage function is assessed. Table 5 captures the climate

impact in 2100 (as a % of GDP) and the 2020 SCC of experiment A (Nordhaus damage functions)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CO2 emissions CO2concentrations

RadiativeForcing

Temperaturechange

GDP per capita Damages SCC, 2020

a2timehprtpt2xco2mueqfex1gsigmaga0_tpopadjust

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CO2 emissions CO2concentrations

RadiativeForcing

Temperaturechange

GDP per capita Damages SCC, 2020

timeh

prtp

t2xco2

mueq

fex1

gsigma

ga0_t

popadjust


and experiment B (Weitzman damage function). The expected climate impact varies enormously

depending of the choice of damage function.

Table 5: Comparison of experiments.

Climate damages in 2100

(% of GDP)

2020 social cost of

carbon

(US$/tCO2)

Experiment A

5% +/- 3% 25 +/- 30

Experiment B

75% +/- 12% 1053 +/- 846

2.5 Discussion and Conclusions Our result show that future economic impacts of climate change are very uncertain and will

depend substantially on parameters such equilibrium climate sensitivity, economic growth and

discount rates and less on others such as population growth or non-CO2 emissions. More importantly

though, the results depend critically on the choice of the damage function which remains highly

contested. The study has some limitations as it only considers first-order effects and, therefore,

in some cases (such as in the case of damages in experiment B where they explain less than 50%

of the outcome) we cannot capture all the existing interactions. This limitation can be improved

in the future.

However, the main message of this study is that even if including an exhaustive list of parameters

and undertaking a complete uncertainty analysis, the dispersion of the values for climate impact

and social cost of carbon will remain high due to uncertainty related to the choice of an

appropriate damage function. The study also shows the risk (i.e. by only performing experiment

A) that applying sophisticated statistical techniques can give us the false impression of precision.


3 ECONOMIC UNCERTAINTY APPRAISAL OF TRANSITION PATHWAYS

3.1 Overview We give an economic evaluation of various uncertainties connected to low-carbon transitions in

European countries (in Northern, Eastern, Central and Southern Europe), Africa (Kenya) and South

America (Chile). Given the relevance of respective regional geographic conditions, institutional

and policy frameworks, we do so within the framework of specific case studies. Chapter 3 in the

following gives an overview of each of these case studies to set the stage for the analysis reported

in Chapter 4.

Using the framework developed and presented in Deliverable 5.1 we here primarily focus on the

quantitative economic evaluation of detrimental and uncertain implications of low-carbon

transitions termed risk. In particular, we study consequential risks (techno-economic, macro-

economic, social, political) when low-carbon transition pathways are followed, here in the context

of each of the case studies. The complementary analysis of implementation risks is reported in

Deliverable D5.2.

To give an overview, we analyse consequential risks of a low-carbon transition in:

- The European steel industry in switching to process-emission free iron and steel production

and its regional economic implications across Europe (case study Austria).

- The electricity power system by small-scale photovoltaic (PV) diffusion in combination with

storage and smart control systems (case study Greece).

- The energy sector in Poland, where still 80% of electricity production depends on coal, and

transformation appears difficult for social (setting free coal miners) and energy cost

reasons (renewables potentially increasing energy prices) (case study Poland).

- The Dutch electricity sector by expanding small-scale PV electricity generation (case study

Netherlands).

- The UK energy system by expansion of nuclear power in consideration of the shift in UK

low-carbon efforts since 2003 and current plans (UK case study).


- The Swiss energy system by an expansion of renewables (domestic and imported solar and

wind electricity) to allow the post-Fukushima nuclear phase-out and reduced foreign fossil

dependency (case study Switzerland).

- The energy supply to private homes in Chile in particular with respect to energy poverty

(case study Chile).

- The energy sector in Kenya (expansion of large hydropower), with particular focus on the

implications on the geothermal and charcoal sub-sectors (case study Kenya)

3.2 Characterisation of Case Studies The sections below contain short descriptions of the TRANSrisk case studies used in this paper. Detailed descriptions are contained in Deliverable D3.2, which is available on the TRANSrisk website.

3.2.1 Austria

The Austrian case study explores possible transitions pathways in the iron and steel industry in a European context. We address the following consequential uncertainties: techno-economic uncertainties in terms of market price for iron and steel as well as sector output, macro-economic uncertainties in terms of gross domestic product (GDP) and welfare as well as social uncertainties in terms of unemployment of the skilled and unskilled labour force. To quantify these uncertainties, we use the WEGDYN model (see chapter 4 for a description), a recursive-dynamic macro-economic computable general equilibrium (CGE) model.

In order to account for uncertainty, we develop different scenarios in which we vary (i) economic, labour force and productivity growth as well as the rate of capital depreciation, subject to different shared socio-economic pathways, (ii) the ambitiousness of climate policy in terms of the level and trajectory of a CO2 tax and (iii) the timing of action of the iron and steel industry in terms of the starting point in time of switching to a carbon-free production technology.

3.2.2 Greece

The quantitative analysis in the present deliverable concerns the specific pathway of achieving a low-carbon power system through empowering consumers to produce and store clean energy at the local level. To this end, the pathway under study focuses on the further diffusion of small-scale PV (1kW-10kW).

Furthermore, and when self-consumption is economically rational, it assumes that consumers will invest in technologies that increase their demand flexibility as a way to increase the proportion


of the self-produced electricity they consume. In this case, diffusion of small-scale PVs is coupled with the diffusion of electricity storage and/or smart heating and cooling control systems.

The main premise of the pathway under study is that the benefits of self-consumption – and with it, the benefits from supporting distributed power generation and demand flexibility – outweigh the costs. Since there is always the possibility that this premise turns out to be false, the quantitative analysis here aims to identify some conditions that could affect this premise. In subsequent deliverables, we will focus on devising policies that are adaptive enough to stay cost-efficient under those conditions.

3.2.3 Poland

Since 1989, Poland has generated substantial economic growth while at the same time managing to significantly reduce its emissions and decrease absolute primary energy consumption, mainly thanks to the restructuring of old and inefficient heavy industry. Despite this progress, Poland’s energy generation system is still heavily reliant on coal (over 80% of electricity is produced from coal) and this will probably continue to be the case in the foreseeable future. If the country wishes to meet environmental targets put forward by the European Commission for 2030 and beyond, the transformation of the energy sector will be a key issue.

Moving away from the coal-based power generation will be a challenge for Poland for several reasons: political, economic, social and technical. Governments in Poland have hesitated to limit the extent of reliance on coal because they see it as a cornerstone of the country’s energy security. Moreover, the major concern is that despite undertaking far reaching reforms in the coal mining sector since the transformation of 1989, it still employs over 100,000. The education and training of these workers is such that their reallocation to other sectors will be problematic, while attempts to reduce employment in coal mining have been met by fierce opposition from various stakeholders such as trade unions. On the other hand, investment in clean energy sources poses a risk of increasing energy prices. This scenario could be problematic if public opinion values economic growth and employment over environmental issues and if there is a high risk of energy poverty. Any transition away from coal towards other energy sources must take these economic and social consequences into account.

3.2.4 Netherlands

The Netherlands is lagging behind on the implementation of renewable energy technologies. Despite the adoption of a new package with measures to intensify renewable energy capacity investment, according to a review by ECN and a recent European Commission report on Member States’ RED compliance performance, the country is expected to fall short of the 14% renewable energy target by 2020. The larger-scale implementation of solar PV panels is among the options to intensify renewable energy production. In 2015, only about 12% of the gross production of the electricity was generated from renewable sources to which solar energy contributed only with 1%.


For this reason, one of the Dutch case studies aims to explore the rapid acceleration of the adoption of solar panels. There are two potential pathways for rapid solar PV expansion in the Dutch electricity sector, which we both assess in this Deliverable:

1. Up-scaling of small-scale solar panel use in the built environment (e.g. on rooftops of households, small businesses, and schools)

2. Large-scale applications of solar panels on land and infrastructure (e.g. solar parks)

Essentially, for reaching the 14% renewable energy target, as well as targets beyond 2020, the Netherlands needs both pathways. However, realizing pathways with an accelerated application of small- and large-scale solar panels is surrounded with risks and uncertainties, such as policy inconsistencies, fiscal and spatial planning issues, impact on other sectors and public acceptance. Our goal is to assess the feasibility of expanding solar PV along these pathways, given the technical potentials, by considering costs and benefits, as well as risks and uncertainties related to the abovementioned aspects.

More information on the Dutch solar case study can be found in the TRANSrisk deliverable 3.2.

3.2.5 United Kingdom

The UK nuclear power case study explores two broad transition pathways: the ´expansion of nuclear power up 40GW´ and the ´no new nuclear power´. These were identified by stakeholders’ consultation in 2016. At present, the UK has around 9 GW of operational nuclear capacity, providing on average some 20% of all UK electricity demand. These power plants are aging and current policy is to close them by 2030, with one exception (Sizewell B) which is expected to close by 2035. However, the UK Government has ambitions for a future build-up of nuclear capacity that is much higher than any other EU-28 country. Only Finland, France, Hungary and Slovakia among the other 27 member states have firm plans to build more reactors and in all cases, the commitment is only to one or two new reactors.

The UK Government’s expectation in 2008 was that installed new nuclear capacity would reach somewhere between 16GW and 75GW by 2050. These ambitious but somewhat imprecise policy ambitions, combined with the controversial nature of the technology, (see D3.2 Context UK Nuclear Power in TRANSrisk Project European Commission, 2016) meant that the future of nuclear power became a clear candidate for the focus of the UK case study. On one hand, nuclear power is a low carbon technology. On the other hand it carries with it large uncertainties across a number of dimensions, including economics, nuclear security, and risks of nuclear weapon proliferation and nuclear terrorism (Cottrell, 2017).

Persistent changes in energy policy since 2003 led to a revival of public support for nuclear power from 2008. In 2015, the Conservative administration announced a ‘policy re-set’, which involved renewed commitment to nuclear power; sharp reductions in subsidies for renewable energy; a probable phase-out of coal-fired power by 2025; abandonment of a number of energy demand


reduction targets; and abandonment of a £1bn commitment to funding Carbon Capture Storage (CCS) technologies. Many of these changes meant reduced support for decarbonisation

A much greater reliance than before is therefore now placed on nuclear power, whether via small modular nuclear reactors (SMRs) or conventional reactors, to deliver the ambitious emission reductions to which the Government remains committed. However, construction of new nuclear capacity has been beset by both ‘internal’ problems (cost escalation, difficulty in agreeing subsidy levels) and ‘external’ problems (the financial fragility of two of the three main potential nuclear suppliers to the UK). This increased reliance on nuclear that is proving economically problematic - in a context in which the rest of the EU is showing signs of either caution or resistance to nuclear technology – is what makes the nuclear power case study so relevant both for the UK and more widely.

3.2.6 Switzerland

This case study focuses on renewable energy in the Swiss context. The driver is the post-Fukushima exit from nuclear power. Nuclear currently produces some 22 TWh, or 31% of Swiss electricity, which is to be replaced with mostly domestic renewable electricity. At the federal level, the political and legislative centrepiece is the Energiestrategie 2050 (ES2050 - Energy Strategy).

In addition to climate change goals and a nuclear phase-out, the Swiss intend to reduce their longstanding reliance on foreign fossil fuels, mostly oil for transportation and heating, as well as some natural gas. However, for all its length, the ES2050 is vague on implementation so far; its main thrusts are efficiency, especially in buildings, e.g. replacing oil heating with more efficient heat pumps, and further electrifying transportation. The first phase of the Swiss ES2050 has been passed into law by parliament in 2016. The Swiss parliament added several changes in the process, including a support package for hydropower, which has been economically less viable due to current low wholesale electricity prices.

In the case study, we examine the technical, economic and social feasibility of generating 30% of the Swiss electricity supply with solar photovoltaics (PV), wind power in Switzerland or imported form the North Sea region, or concentrating solar power (CSP) imported from North Africa, using the Calliope model (see section 4.1.2).

3.2.7 Chile

In Chile and the world, sustainable development is becoming increasingly important, either because of the societal implications of the climate change impacts, or because of the importance and costs of local pollution in each country. One of the main issues in this context is poverty and energy vulnerability. In the Chilean context, there are three factors that motivate us to measure, evaluate and design public policies regarding energy poverty. There three factors are climate change and cost to energy, energy consumption of householdes and poverty reduction. With these


three joint factors that characterise Chile's development path, the dimension of energy poverty is particularly interesting to study. While society seeks satisfaction of more complex needs (as are their energy needs) subject to the availability of resources, there are new challenges that need to be considered, such as the need to contribute to the mitigation of environmental externalities, the incorporation of the carbon price, and the promotion of the change in the structure of energy consumption in Chilean homes.

3.2.8 Kenya

Like many countries in sub-Saharan Africa, Kenya has high development ambitions, aiming to become a middle-income country by 2030. In particular, these ambitions are based upon a low-carbon, climate resilient development pathway, as set out by Kenya’s Intended Nationally Determined Contributions (INDC). These development ambitions depend on the rapid expansion of the energy sector to increase the access, security and affordability of energy service provision. Geothermal seems poised to play an important catalysing role in this expansion. At the same time, a narrow focus on power sector development overlooks the important role of biomass energy for cooking, particularly charcoal, which is the preferred cooking fuel for a rapidly expanding urban population and has significant impact on land use emissions. With this in mind, this case study explores the energy sector in Kenya, with particular focus on the geothermal and charcoal sub-sectors.


4 ECONOMIC EVALUATION OF CONSEQUENTIAL RISKS OF TRANSITION PATHWAYS

4.1 Methods To address each of the low-carbon risks with an adequate modelling approach, the economic quantitative evaluation of consequential risks employs a range of different modelling approaches (econometric, computable general equilibrium, agent based modelling, strategy modelling) and different respective models. Before reporting their quantitative results across the various case study applications, we first give an account of each of these specific models. These models have been applied for the analyses of low-carbon transitions in the case study contexts, usually each model in more than one case study context, and sometimes aspects within a single case study were analysed by more than one modelling approach, with these results reported thereafter in sections 4.2 to 4.9 and structured by case study context.

With each of the case studies having progressed to different stages, also the depth of quantitative modelling already accomplished is different across case studies, which is reflected in the results reported in this Deliverable. In a qualitative approach we discuss further economic risks that either do not lend themselves to quantitative analysis or will only be quantitatively implemented based also on the qualitative results reported here.

4.1.1 The BSAM Model

The Business Strategy Assessment Model (BSAM) includes (Papadelis et al., 2012):

- An electricity market module that simulates the operation of a wholesale electricity market (bidding agents and clearance mechanism) to quantify the effects of different policy measures and market developments on the electricity price and the fuel mix, and

- An agent-based model that simulates power bidding and investment decisions by electricity generators, and which is being adapted, in the framework of WP6 of TRANSrisk, to model investment decisions by prosumers (in small-scale PVs and technologies that enable flexibility of energy consumption).

The model has been used in the FP7 project APRAISE3 for the ex-post assessment of energy policy measures and their interrelationships.

The overall workflow for the quantitative analysis carried out by BSAM across the TRANSrisk WPs (WP3, WP5, WP6) is presented in Figure 4. In particular:

3Assessment of Policy Interrelationships and Impacts on Sustainability in Europe (2012-2014).


1. The scenarios for the evolution of the market share of small-scale PVs are the result of an agent-based model (ABM) that correlates technology adoption with its value for the consumers. The main purpose of the ABM model is to capture a central source of (epistemic) uncertainty that is the actual relationship between technology adoption and its value for the consumers. In particular, we cannot know the value of the corresponding investments that will trigger their adoption, as well as the size of the installed base that is necessary for consumers to start evaluating the corresponding investments.

2. The quantification of the PV value for consumers takes place based on the main drivers that determine the profitability of these investments: support schemes, price of electricity, as well as technology costs and efficiency.

3. Policy assessment is carried out according to: a) the attainment of a nominal plan of PV expansion and b) the benefit-to-cost performance of the policy.

;

Figure 4: The workflow for the quantitative analysis carried out by BSAM.

It should be noted that all agents affect each other only indirectly through their environment rather than through bilateral interactions. Agents that represent power generators learn (stable) bidding strategies by taking into consideration the observed bidding behaviour of their competitors. Agents that represent consumers becoming prosumers make their decisions based on: a) their internal characteristics that are randomly assigned during the scenario preparation phase, and b) the way their environment changes according to the decisions of their peers4.

4.1.2 The Calliope Energy Modelling Framework

4 Including technology popularity and trends, in order to capture positive or negative feedback effects (e.g. herd effects).


As the documentation of the model frameworks states: "Calliope is a framework to develop energy system models using a modern and open source Python-based toolchain. It is under active development and freely available under the Apache 2.0 license." 5 The Calliope framework is used to build cost optimisation models of energy systems with high temporal and varying levels of spatial disaggregation. It can use hourly data on supply potentials and demand on geographical areas varying from nations to city blocks, all in the same model.

Calliope is best used for medium-sized models, rather than full-blown integrated assessment models. So far, it has been used for time- and/or location-sensitive planning for electricity systems that exhibit dai

deliverable 5.3: economic uncertainty...

Documents